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Mar, Arthur

					
				
									

						
Bio

						
Arthur Mar leads an outstanding research program in inorganic solid-state chemistry at the University of Alberta.  He is an international leader recognized for his expertise in the synthesis of extended inorganic solids, especially intermetallic compounds and Zintl phases representing the broad diversity of the periodic table, encompassing rare-earth elements, transition metals, and the less-electronegative p-block elements.  Over the past 25 years, Mar has attained a distinguished international reputation in intermetallic chemistry, having published >213 articles (including the only authoritative review on rare-earth bismuthides) and given >100 invited presentations.  He has served on the editorial boards of Chemistry of Materials, Journal of Solid State Chemistry, and Acta Crystallographica.  In addition to being recognized as an excellent researcher (through the Faculty of Science Research Award at the University of Alberta), he is an outstanding, award-winning teacher who is highly valued for his enthusiastic contributions to chemical education.  He was a Professeur invité at the Université de Rennes 1 (2003), a Distinguished Visiting Scholar at Beijing Normal University (2017), and a Distinguished Overseas Professor at Shanghai University of Engineering Science (2019).
					

				
									
				
									
				
			


			

				
					Mar, Arthur
				

				
				
			


		


	

		

			


								

					
Publications, Activities, and Awards

					
    
													
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																	Accelerated discovery of perovskites and prediction of band gaps using machine-learning methods															
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																	Accelerating the Discovery of Materials with Machine Learning: Potential Roadblocks and How to Overcome Them															
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																	Accelerating the Discovery of Materials with Machine Learning: Potential Roadblocks and How to Overcome Them															
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																	Accelerating the Discovery of Materials: Machine-Learning Approach															
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																	Accelerating the Discovery of Solid State Materials with Machine-Learning Approaches															
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																	Accelerating the Discovery of Solid State Materials:  From Traditional to Machine-Learning Approaches															
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																	Accelerating the Discovery of Solid State Materials:  From Traditional to Machine-Learning Approaches															
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																	Accelerating the Discovery of Solid State Materials:  From Traditional to Machine-Learning Approaches															
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																	Accelerating the discovery of solid state materials: From traditional to machine- learning approaches															
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																	Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches															
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																	Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches															
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																	Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches															
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																	Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches															
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																	Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches															
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																	Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches															
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																	Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approachs															
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																	Alkaline Earth Metal-Organic Frameworks with Tailorable Ion Release: A Path for Supporting Biomineralization
															
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																	Classification of Half-Heusler Compounds through Machine Learning Approaches															
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																	Classification of Half-Heusler Compounds through Machine-Learning Approaches															
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																	Computational workshop															
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																	Crystallography in Chemistry and Materials Science															
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																	Design of Experiments and Machine Learning-Assisted Organic Solar Cell Efficiency Optimization
															
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																	Discovery of Intermetallic Compounds from Traditional to Machine-Learning Approaches															
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																	Discovery of Noncentrosymmetric Ternary Compounds from Elemental Composition: A Machine-Learning Approach															
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																	Discovery of ternary noncentrosymmetric compounds:  A machine-learning approach with experimental proof															
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																	Disentangling Structural Confusion through Machine Learning: Structure Prediction and Polymorphism of Equiatomic Ternary Phases ABC															
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																	Effectively Exploring Parameter Space: Design of Experiments and Machine Learning-assisted Organic Solar Cell Efficiency Optimization
															
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																	Excellence in Undergraduate Teaching 2017															
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																	Excellence in Undergraduate Teaching 2018															
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																	Exploring the colours of gold alloys with machine learning															
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																	Faculty of Science Students' Choice Honour Roll 															
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																	Future Energy Systems Research Symposium															
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																	Hexagonal Double Perovskite Cs2AgCrCl6															
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																	High-Throughput Approaches for Discovering Thermoelectric Materials															
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																	How large is an atom?															
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																	How to look for compounds															
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																	How to look for compounds															
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																	How to look for compounds															
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																	How To Optimize Materials and Devices via Design of Experiments and Machine Learning: Demonstration Using Organic Photovoltaics
															
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																	In Search of Coloured Intermetallics															
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																	Influence of hidden halogen mobility on local structure of CsSn (Cl1-xBrx)3 mixed-halide perovskites by solid-state NMR
															
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																	Introduction to Machine Learning: A Practical Workshop															
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																	Invited talk: ACS national meeting, Boston 
															
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																	Machine Learning and Models: How we find optimal materials for Solar and CCS technologies															
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																	Machine-learning predictions of half-Heusler structures															
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																	Not Just Par for the Course: 73 Quaternary Germanides RE4M2XGe4 (RE = La–Nd, Sm, Gd–Tm, Lu; M = Mn–Ni; X = Ag, Cd) and the Search for Intermetallics with Low Thermal Conductivity															
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																	Prediction of Novel Compounds and Rapid Property Screening through a Machine Learning Approach															
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																	Quarternary Rare-earth Transition-Metal Germanides: RE4M2CdGe4 and RE4M2AgGe4 (RE=La-SM, Gd-Lu, M=Mn-Ni)															
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																	Quaternary rare-earth sulfides RE3M0.5M'S7 (M = Zn, Cd; M' = Si, Ge)															
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																	Quaternary Rare-Earth Transition-Metal Germanides RE4M2CdGe4 and RE4M2AgGe4 (RE = La–Sm, Gd–Tm, Lu; M = Mn–Ni)															
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																	Rare-earth transition-metal oxyselenides															
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																	Searching for Missing Binary Equiatomic Phases:  Complex Crystal Chemistry in the Hf–In System															
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																	Solving the Colouring Problem in Half-Heusler Structures:  Machine-Learning Predictions and Experimental Validation
															
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																	Structure and Luminescence Properties of Rare-Earth Chalcohalides RE3Ge2Ch8X (Ch = S,  Se; X = Cl, Br, I)															
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																	Synthesis, structure, and properties of rare-earth germanium sulfide iodides RE3Ge2S8I (RE = La, Ce, Pr)															
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																	Ternary and Quaternary Rare-Earth Transition-Metal Germanides															
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																	Ternary and quaternary rare‐earth germanides: discovery of intermetallic compounds from traditional to machine‐learning approaches															
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																	Ternary Germanides in Ce-M-Ge System (M=Rh, Co)															
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																	Ternary Germanides in the Ce–M–Ge (M = Rh, Co) Systems															
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																	Thermoelectric properties of inverse perovskites A3TtO (A = Mg, Ca; Tt = Si, Ge): Computational and experimental investigations															
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																	USchool:  Materials and Informatics															
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																	X-ray diffraction short course															
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